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Semiparametric Regression Analysis of Longitudinal Skewed Data

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  • Huazhen Lin
  • Ling Zhou
  • Xiaohua Zhou

Abstract

type="main" xml:id="sjos12080-abs-0001"> In this paper, we develop a semiparametric regression model for longitudinal skewed data. In the new model, we allow the transformation function and the baseline function to be unknown. The proposed model can provide a much broader class of models than the existing additive and multiplicative models. Our estimators for regression parameters, transformation function and baseline function are asymptotically normal. Particularly, the estimator for the transformation function converges to its true value at the rate n-super- − 1 ∕ 2, the convergence rate that one could expect for a parametric model. In simulation studies, we demonstrate that the proposed semiparametric method is robust with little loss of efficiency. Finally, we apply the new method to a study on longitudinal health care costs.

Suggested Citation

  • Huazhen Lin & Ling Zhou & Xiaohua Zhou, 2014. "Semiparametric Regression Analysis of Longitudinal Skewed Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 1031-1050, December.
    • Handle: RePEc:bla:scjsta:v:41:y:2014:i:4:p:1031-1050
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    References listed on IDEAS

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    Cited by:

    1. Chen, Xuerong & Li, Haoqi & Liang, Hua & Lin, Huazhen, 2019. "Functional response regression analysis," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 218-233.

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